Evidence map›Paper›PMID 37338599›Full record

ReviewFunctional & integrative genomics2023

Genome engineering of disease susceptibility genes for enhancing resistance in plants.

Ritika Bishnoi, Sehgeet Kaur, Jagdeep Singh Sandhu, Deepak Singla

Abstract readReview
PubMed Publisher
In one paragraph

Review in Functional & integrative genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
7.5field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 19 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Ritika BishnoiBioinformatics Centre, School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India. ritikabishnoi86@pau.edu.
Sehgeet KaurBioinformatics Centre, School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India.
Jagdeep Singh SandhuSchool of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India.
Deepak SinglaBioinformatics Centre, School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India. deepak@pau.edu.
Punjab Agricultural University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introgression of disease resistance genes (R-genes) to fight against an array of phytopathogens takes several years using conventional breeding approaches. Pathogens develop mechanism(s) to escape plants immune system by evolving new strains/races, thus making them susceptible to disease. Conversely, disruption of host susceptibility factors (or S-genes) provides opportunities for resistance breeding in crops. S-genes are often exploited by phytopathogens to promote their growth and infection. Therefore, identification and targeting of disease susceptibility genes (S-genes) are gaining more attention for the acquisition of resistance in plants. Genome engineering of S-genes results in targeted, transgene-free gene modification through CRISPR-Cas-mediated technology and has been reported in several agriculturally important crops. In this review, we discuss the defense mechanism in plants against phytopathogens, tug of war between R-genes and S-genes, in silico techniques for identification of host-target (S-) genes and pathogen effector molecule(s), CRISPR-Cas-mediated S-gene engineering, its applications, challenges, and future prospects.

Indexed as

Genome, PlantPlant DiseasesCRISPR-Cas SystemsCrops, AgriculturalPlant BreedingPlants, Genetically ModifiedCRISPR-CasDatabaseEffectorsGenome editingGenome engineeringResistance genesSusceptibility genes

Identifiers

PMID37338599
OpenAlexW4381308058

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.